Solution:Step 1: Perform QR decomposition on matrix AMatrix A is [[1,0],[1,1]]. QR decomposition produces orthogonal Q and upper triangular R.Step 2: Calculate Q and round valuesQ columns are orthonormal vectors. The output rounded to 2 decimals is [[0.71, -0.71],[0.71, 0.71]].Final Answer:[[ 0.71 -

[Solved] Given the code:import numpy as np from scipy.linalg import qr A = np.array([[1, 0], [1, 1]]) Q, R = qr(A) print(np.round(Q, 2))What is the output? | SciPy

SciPy - Linear Algebra (scipy.linalg)

Given the code:

import numpy as np
from scipy.linalg import qr
A = np.array([[1, 0], [1, 1]])
Q, R = qr(A)
print(np.round(Q, 2))

What is the output?

A[[ -0.71 0.71] [ -0.71 -0.71]]

B[[ 0.71 0.71] [ 0.71 -0.71]]

C[[ 0.71 -0.71] [ 0.71 0.71]]

D[[ 1.00 0.00] [ 0.00 1.00]]

Step-by-Step Solution

Solution:

Step 1: Perform QR decomposition on matrix A
Matrix A is [[1,0],[1,1]]. QR decomposition produces orthogonal Q and upper triangular R.
Step 2: Calculate Q and round values
Q columns are orthonormal vectors. The output rounded to 2 decimals is [[0.71, -0.71],[0.71, 0.71]].
Final Answer:
[[ 0.71 -0.71] [ 0.71 0.71]] -> Option C
Quick Check:
Q matrix output matches [[ 0.71 -0.71] [ 0.71 0.71]] [OK]

Quick Trick: Q columns are orthonormal vectors from A [OK]

Common Mistakes:

MISTAKES

Ignoring sign differences in Q
Confusing Q with R matrix output
Not rounding output correctly

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Given the code:

Step 1: Perform QR decomposition on matrix A

Step 2: Calculate Q and round values

Final Answer:

Quick Check:

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